Source Data

The data used in this project was uploaded by the user Plummye on Kaggle.com. It contains information on all Scooby-Doo episodes, movies and specials as of 9/15/2021. The data has 75 columns covering numerous variables including show/movie information, monster information, culprit information, motives, character appearances, and gags(jinkies, zoinks, lost glasses, traps set, etc).

Objective

I was watching ‘Scooby-Doo: Where Are You?’ just as the culprit was being unmasked. This caused me to muse on the motives of most Scooby-Doo villains, especially considering how far they go to achieve their goal. In this analysis, I will examine the motives of the villains from several Scooby-Doo tv shows, featuring the classic gang of Fred, Daphne, Velma, Shaggy and Scooby-Doo.

Task

  1. Determine the top and bottom five overall motives from all series
  2. Examine the motives between male and female culprits
  3. Identify the top three motives for each show
  4. Identify the top three motives per environment and year

Step 1: Get Data

Load Library

#data manipulation
library(tidyr)
library(tidyverse)
library(dplyr)
library(lubridate)

#documentation & markdown
library(knitr)

#creating charts & interactive plots 
library(ggplot2)
library(plotly)
library(webshot) #screenshot of plotly chart in markdown

Import Data

df <- read.csv('Scooby-Doo Completed.csv')

Step 2: Clean & Modify Data

Select and Inspect Data

#copy data 
sc_df <- df

#select columns
sc_df <- sc_df[,c(2,8,10,42,48,50)]

#view column names 
colnames(sc_df)
## [1] "series.name"     "date.aired"      "format"          "setting.terrain"
## [5] "culprit.gender"  "motive"

Check for Missing/NA values

sapply(sc_df, function(x) sum(is.na(x)))
##     series.name      date.aired          format setting.terrain  culprit.gender 
##               0               0               0               0               0 
##          motive 
##               0

Check for Empty/Blank Values

sapply(sc_df, function(x) sum(x==""))
##     series.name      date.aired          format setting.terrain  culprit.gender 
##               0               0               0               0               0 
##          motive 
##               0

Filter and Modify Data

#copy data just in case
scoob <- sc_df

#filter data to: include only the tv shows
                #include only shows with the full Scooby-Doo 
                #remove NULL values from motive column
scoob <- scoob %>%
  filter((format == "TV Series") | (format == "TV Series (segmented)")) %>%
  filter((series.name != "Lego") & (series.name  != "Warner Home Video") &
         (series.name != "Shaggy & Scooby-Doo Get a Clue!") & (series.name  != "Night of the Living Doo") &
         (series.name != "The Scooby-Doo Project") & (series.name  != "The 13 Ghosts of Scooby-Doo") &
         (series.name != "The New Scooby and Scrappy Doo Show") & (series.name  != "Scooby-Doo and Scrappy-Doo (second series)") &
         (series.name != "Scooby-Doo and Scrappy-Doo (first series)") & (series.name  != "Laff-a-Lympics")) %>%
  filter(motive != "NULL")

#change data type 
scoob$date.aired <- as.Date(scoob$date.aired)

#drop format column
scoob <- scoob[-c(3)]

#rename columns
scoob <- rename(scoob, series=series.name, environment=setting.terrain)

View Data

str(scoob)
## 'data.frame':    324 obs. of  5 variables:
##  $ series        : chr  "Scooby Doo, Where Are You!" "Scooby Doo, Where Are You!" "Scooby Doo, Where Are You!" "Scooby Doo, Where Are You!" ...
##  $ date.aired    : Date, format: "1969-09-13" "1969-09-20" ...
##  $ environment   : chr  "Urban" "Coast" "Island" "Cave" ...
##  $ culprit.gender: chr  "Male" "Male" "Male" "Male" ...
##  $ motive        : chr  "Theft" "Theft" "Treasure" "Natural Resource" ...
## Warning in rm(df, sc_df, scd): object 'scd' not found

Step 3: Analyze Data

summary(scoob)
##     series            date.aired         environment        culprit.gender    
##  Length:324         Min.   :1969-09-13   Length:324         Length:324        
##  Class :character   1st Qu.:1978-11-02   Class :character   Class :character  
##  Mode  :character   Median :2004-03-23   Mode  :character   Mode  :character  
##                     Mean   :1998-11-01                                        
##                     3rd Qu.:2015-10-24                                        
##                     Max.   :2021-02-25                                        
##     motive         
##  Length:324        
##  Class :character  
##  Mode  :character  
##                    
##                    
##

Top & Bottom 5 Motives from All Episodes

scoob %>%
  group_by(motive) %>%
  summarise(count=n()) %>%
  arrange(desc(count)) %>%
  slice(unique(c(1:5, n() - 4:0))) 
## # A tibble: 10 × 2
##    motive           count
##    <chr>            <int>
##  1 Competition        127
##  2 Theft               87
##  3 Treasure            36
##  4 Natural Resource    19
##  5 Smuggling           17
##  6 Entertainment        2
##  7 Extortion            2
##  8 Safety               2
##  9 Simulation           2
## 10 Imagination          1

Top 3 Motives by Series

scoob %>%
  group_by(series, motive) %>%
  reframe(count=n()) %>%
  arrange(series, desc(count)) %>%
  group_by(series) %>%
  slice(1:3)
## # A tibble: 27 × 3
## # Groups:   series [9]
##    series                          motive      count
##    <chr>                           <chr>       <int>
##  1 A Pup Named Scooby-Doo          Competition    11
##  2 A Pup Named Scooby-Doo          Theft          10
##  3 A Pup Named Scooby-Doo          Assistance      2
##  4 Be Cool, Scooby-Doo!            Competition    30
##  5 Be Cool, Scooby-Doo!            Theft          13
##  6 Be Cool, Scooby-Doo!            Treasure        4
##  7 Scooby Doo, Where Are You!      Theft          11
##  8 Scooby Doo, Where Are You!      Competition     4
##  9 Scooby Doo, Where Are You!      Treasure        3
## 10 Scooby-Doo Mystery Incorporated Competition    22
## # ℹ 17 more rows

Motive Count by Gender

scoob %>%
  group_by(culprit.gender, motive) %>%
  separate_rows(culprit.gender, sep = ",") %>%
  mutate(culprit.gender=trimws(culprit.gender)) %>%
  group_by(culprit.gender, motive) %>%
  reframe(count=n()) %>%
  arrange(motive, desc(count)) %>%
  filter(culprit.gender != "NULL")
## # A tibble: 27 × 3
##    culprit.gender motive        count
##    <chr>          <chr>         <int>
##  1 Male           Abduction         2
##  2 Female         Assistance        1
##  3 Male           Assistance        1
##  4 Male           Competition     128
##  5 Female         Competition      43
##  6 Male           Conquer           1
##  7 Male           Counterfeit       8
##  8 Female         Counterfeit       2
##  9 Male           Entertainment     3
## 10 Male           Extortion         2
## # ℹ 17 more rows

Top 3 Motives by Environment

scoob %>%
  group_by(environment, motive) %>%
  reframe(count=n()) %>%
  arrange(environment, desc(count)) %>%
  group_by(environment) %>%
  slice(1:3)
## # A tibble: 38 × 3
## # Groups:   environment [14]
##    environment motive      count
##    <chr>       <chr>       <int>
##  1 Air         Competition     1
##  2 Cave        Treasure        4
##  3 Cave        Competition     1
##  4 Cave        Conquer         1
##  5 Coast       Competition     3
##  6 Coast       Smuggling       2
##  7 Coast       Counterfeit     1
##  8 Desert      Competition     9
##  9 Desert      Theft           3
## 10 Desert      Treasure        2
## # ℹ 28 more rows

Top 3 Motives by Year

scoob %>%
  mutate(aired.year=year(date.aired)) %>%
  group_by(aired.year, motive) %>%
  reframe(count=n()) %>%
  arrange(aired.year, desc(count)) %>%
  group_by(aired.year) %>%
  slice(1:3)
## # A tibble: 78 × 3
## # Groups:   aired.year [28]
##    aired.year motive      count
##         <dbl> <chr>       <int>
##  1       1969 Theft           5
##  2       1969 Competition     4
##  3       1969 Treasure        2
##  4       1970 Theft           6
##  5       1970 Smuggling       2
##  6       1970 Inheritance     1
##  7       1972 Theft           5
##  8       1972 Treasure        3
##  9       1972 Competition     2
## 10       1973 Competition     6
## # ℹ 68 more rows

Step 4: Visualize Data